Simulating desired speeds-based intelligent driver model for large sample size of urban expressways

Abstract

The best car-following model (Intelligent Driver Model) incorporates desired speed parameter, whereas the literature suggested to include such parameter in driving behavior of lane changing model. Previous researches, however, have overlooked few things that desired speed values of many vehicles are to be collected from big data, and these values may have a significant effect on discretionary lane changing action. This research proposes the desired speed values for lane changing drivers and target lane vehicle drivers from calibrated IDM using big data for on-ramp and off-ramp areas, and simulates this IDM using the proposed data for validation test. The calibration method uses a genetic algorithm against the real dataset. Further, finding results suggest overcoming conflicts in this dataset by controlling the used dynamic factors. High performance-based traffic simulation software in the future can use the further developed model to decrease traffic crashes, bottlenecks, and long signals in the intersection.